ML Fundamentals

Why machine learning systems are fundamentally different and require new engineering approaches.

25 min readBeginner
Not Started
Loading...

Machine learning systems are not just "traditional systems with models added." They introduce fundamentally new failure modes, testing requirements, and operational challenges. Understanding these differences is crucial for building reliable ML products.

Traditional software engineering practices don't directly apply to ML systems. You need new tools, processes, and mindsets to handle the unique challenges of probabilistic systems that depend on data quality.

⚡ Quick Decision

Start with Traditional When:

  • • Rules-based solution possible
  • • Deterministic outcomes required
  • • Small, stable datasets

Consider ML When:

  • • Pattern recognition needed
  • • Large datasets available
  • • Human judgment expensive

Avoid ML When:

  • • No clear success metrics
  • • Insufficient data
  • • High-stakes decisions only

Traditional vs ML Systems Comparison

🔧

System Complexity

🏛️ Traditional Systems

Code defines behavior
Linear input/output
Deterministic results
Easy to debug
Well-established patterns

🤖 ML Systems

Data + Code + Model define behavior
Complex feature interactions
Probabilistic outputs
Hard to debug (black box)
Rapidly evolving patterns

Unique ML System Challenges

Data DependenciesVery High Impact
Input data changes break models in subtle ways
Example: Feature engineering change upstream affects 5 downstream models
Configuration ComplexityHigh Impact
ML systems have exponentially more configuration than traditional systems
Example: Hyperparameters, feature flags, model versions, data sources
Model Performance DecayHigh Impact
Models degrade over time as real-world data drifts
Example: COVID-19 broke all e-commerce recommendation models
Feedback LoopsMedium Impact
Model predictions influence future training data
Example: Search ranking affects what users click, biasing future models
Distributed System ComplexityMedium Impact
Training and serving often require different infrastructure
Example: GPU clusters for training, CPU clusters for serving

💰 Hidden Costs of ML Systems

Infrastructure Costs

GPU Training: $2-10/hour vs $0.01/hour CPU
Data Storage: Raw + processed + model artifacts
Model Serving: Real-time inference requires low latency
Experimentation: Multiple training runs, A/B tests

Engineering Costs

Data Engineering: 60-80% of ML project time
Model Monitoring: Drift detection, alerting, retraining
Feature Engineering: Complex pipelines, versioning
Debugging: Non-deterministic failures are hard to reproduce

🎯 Success Patterns

📊
Start Simple
Begin with basic models and iterate. Linear regression often beats complex neural networks.
🔄
Invest in Data
Quality data pipelines matter more than sophisticated algorithms.
📈
Monitor Everything
ML systems fail silently. Comprehensive monitoring prevents disasters.

📝 ML Fundamentals Mastery Check

1 of 3Current: 0/3

What causes most ML system failures in production?